Computer systems, and the devices and applications that define them, commonly report events as semi-structured records written to log files. These records conform to patterns of text fields. The lack of common standards, the introduction of localizations, and the desire to describe many types of events (e.g., log, audit, and trace-back) makes parsing of the records complex. To extract meaning from this information, regular expressions have been used to parse the log records into fields to produce consistently formatted and meaningful log events. However, parsing a given log record first requires identifying the log record's type, for example, based on its source. A log file with multiple record types or a merged log stream from multiple sources, challenges identification as the number of log record regular expressions increases resulting in central processing unit (CPU) intensive detection processing with significant scalability limitations. Nevertheless, processing a large system's log events as a single stream facilitates discovery of correlations that enables faster recognition of issues and improves the reliability of a system. As a result, log record parsing into uniform events can benefit substantially from efficient log record identification.